What is Spectral Clustering?
Spectral clustering is an advanced machine learning technique used for unsupervised learning, particularly in the field of Artificial Intelligence. It is designed to categorize data points into distinct groups based on their similarities. Unlike traditional clustering methods like K-means, spectral clustering leverages the properties of graph theory and the eigenvalues of matrices to identify clusters in data that may not be linearly separable.
How it Works
The process begins by representing the data as a similarity graph, where nodes correspond to data points and edges represent the similarity between those points. The next step involves constructing a Laplacian matrix, which encapsulates the structure of the graph. By calculating the eigenvalues and eigenvectors of this matrix, one can reduce the dimensionality of the data while preserving its intrinsic structure.
Key Steps
- Create a similarity graph from the dataset.
- Compute the Laplacian matrix of the graph.
- Determine the eigenvalues and eigenvectors.
- Use the top eigenvectors to form a new matrix.
- Apply a standard clustering algorithm (e.g., K-means) on this new representation.
Applications
Spectral clustering is widely used in various domains, including image segmentation, social network analysis, and bioinformatics. Its ability to handle complex cluster shapes and structures makes it a powerful tool in exploring data in fields that require sophisticated analysis.